Molecular basis of mood and cognitive adverse events elucidated via a combination of pharmacovigilance data mining and functional enrichment analysis.
Christos AndronisJoão Pedro SilvaEftychia LekkaVassilis VirvilisHelena CarmoKonstantina BampaliMargot ErnstYang HuIrena LoryanJacques RichardFelix CarvalhoMiroslav M SavićPublished in: Archives of toxicology (2020)
Drug-induced Mood- and Cognition-related adverse events (MCAEs) are often only detected during the clinical trial phases of drug development, or even after marketing, thus posing a major safety concern and a challenge for both pharmaceutical companies and clinicians. To fill some gaps in the understanding and elucidate potential biological mechanisms of action frequently associated with MCAEs, we present a unique workflow linking observational population data with the available knowledge at molecular, cellular, and psychopharmacology levels. It is based on statistical analysis of pharmacovigilance reports and subsequent signaling pathway analyses, followed by evidence-based expert manual curation of the outcomes. Our analysis: (a) ranked pharmaceuticals with high occurrence of such adverse events (AEs), based on disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) database, and (b) identified 120 associated genes and common pathway nodes possibly underlying MCAEs. Nearly two-thirds of the identified genes were related to immune modulation, which supports the critical involvement of immune cells and their responses in the regulation of the central nervous system function. This finding also means that pharmaceuticals with a negligible central nervous system exposure may induce MCAEs through dysregulation of the peripheral immune system. Knowledge gained through this workflow unravels putative hallmark biological targets and mediators of drug-induced mood and cognitive disorders that need to be further assessed and validated in experimental models. Thereafter, they can be used to substantially improve in silico/in vitro/in vivo tools for predicting these adversities at a preclinical stage.
Keyphrases
- clinical practice
- drug induced
- adverse drug
- liver injury
- electronic health record
- bipolar disorder
- clinical trial
- signaling pathway
- healthcare
- genome wide
- gene expression
- big data
- risk assessment
- machine learning
- emergency department
- palliative care
- squamous cell carcinoma
- oxidative stress
- bioinformatics analysis
- stem cells
- cerebrospinal fluid
- transcription factor
- artificial intelligence
- cross sectional
- bone marrow
- metabolic syndrome
- lymph node
- adipose tissue
- data analysis
- early stage
- locally advanced
- deep learning
- human health
- rectal cancer
- single molecule
- open label
- genome wide analysis